Adaptive Mission Abort Planning Integrating Bayesian Parameter Learning

Abstract

Failure of a safety-critical system during mission execution can result in significant financial losses. Implementing mission abort policies is an effective strategy to mitigate the system failure risk. This research delves into systems that are subject to cumulative shock degradation, considering uncertainties in shock damage. To account for the varied degradation parameters, we employ a dynamic Bayesian learning method using real-time sensor data for accurate degradation estimation. Our primary focus is on modeling the mission abort policy with an integrated parameter learning approach within the framework of a finite-horizon Markov decision process. The key objective is to minimize the expected costs related to routine inspections, system failures, and mission disruptions. Through an examination of the structural aspects of the value function, we establish the presence and monotonicity of optimal mission abort thresholds, thereby shaping the optimal policy into a controlled limit strategy. Additionally, we delve into the relationship between optimal thresholds and cost parameters to discern their behavior patterns. Through a series of numerical experiments, we showcase the superior performance of the optimal policy in mitigating losses compared with traditional heuristic methods.